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Abstract

Systemic-onset juvenile idiopathic arthritis (SJIA) is a disease of unknown etiology
with an unpredictable response to treatment. We examined two groups of patients to
determine whether there are serum protein profiles reflective of active disease and
predictive of response to therapy. The first group (n = 8) responded to conventional therapy. The second group (n = 15) responded to an experimental antibody to the IL-6 receptor (MRA). Paired sera
from each patient were analyzed before and after treatment, using surface-enhanced
laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Despite
the small number of patients, highly significant and consistent differences were observed
before and after response to therapy in all patients. Of 282 spectral peaks identified,
23 had mean signal intensities significantly different (P < 0.001) before treatment and after response to treatment. The majority of these differences
were observed regardless of whether patients responded to conventional therapy or
to MRA. These peaks represent potential biomarkers of active disease. One such peak
was identified as serum amyloid A, a known acute-phase reactant in SJIA, validating
the SELDI-TOF MS platform as a useful technology in this context. Finally, profiles
from serum samples obtained at the time of active disease were compared between the
two patient groups. Nine peaks had mean signal intensities significantly different
(P < 0.001) between active disease in patients who responded to conventional therapy
and in patients who failed to respond, suggesting a possible profile predictive of
response. Collectively, these data demonstrate the presence of serum proteomic profiles
in SJIA that are reflective of active disease and suggest the feasibility of using
the SELDI-TOF MS platform used as a tool for proteomic profiling and discovery of
novel biomarkers in autoimmune diseases.

Introduction

Systemic-onset juvenile idiopathic arthritis (SJIA) is a form of childhood arthritis
of unknown etiology, characterized by systemic features in addition to arthritis,
including spiking fever, erythematous rash, articular involvement, and other, visceral
manifestations [1]. Its clinical course is associated with changes in the levels of several serum proteins,
including IL-6 [2]. Over half of children with SJIA eventually recover almost completely [3]. The other half have severe, unremitting arthritis, poorly responsive to conventional
therapy, leading to poor functional outcome and substantial morbidity [4]. In view of the heterogeneity of clinical disease manifestations and the unpredictability
of treatment responses in SJIA, there would be great clinical benefit in the discovery
of biomarkers reflective of disease activity and predictive of response to therapy.

Proteomics, or protein pattern analysis, is the characterization and quantitation
of proteins in tissues and body fluids [5]. Proteomic methods can be used to compare protein expression patterns between disease
states. Although two-dimensional gel electrophoresis has been the primary technique
in conventional proteomic analysis, it is relatively insensitive to proteins of low
abundance and below 10 kDa in mass, is labor intensive, and has low throughput. A
more recent technology known as surface-enhanced laser desorption/ionization time-of-flight
mass spectrometry (SELDI-TOF MS), a derivative of conventional matrix-associated laser
desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), involves the
application of a biologic sample, such as serum, to a protein-binding chip [6]. The chip is irradiated with a laser, resulting in ionization of the adherent molecules.
The ions travel through a vacuum tube and their mass-to-charge ratios are calculated
from their time of flight through the vacuum chamber. The technology is high throughput,
rapid, and sensitive and provides a profile of low-molecular-weight peptides and proteins
within a complex mixture such as serum.

SELDI-TOF MS does not directly identify specific proteins. It has been used to differentiate
disease states from nondisease states by analysis of protein profiles in sera. Examples
include the differentiation of neoplastic from non-neoplastic breast masses [7], prognostic and diagnostic classification of breast cancer [8], neoplastic versus non-neoplastic disease of the ovary [9], and prostate cancer from both men with benign hyperplasia and healthy men [10]. SELDI-TOF MS has also been used for the discovery of disease-related biomarkers
in sera. Examples include detection of serum amyloid α in patients with renal cancer
[11] and the quantitation of prostate-specific membrane antigen in prostate cancer [12].

The present study was designed to determine whether there are serum proteomic profiles
in SJIA that are reflective of active disease and predictive of response to therapy,
as well as to determine whether SELDI-TOF MS could be used as a tool for proteomic
profiling and for discovery of novel biomarkers of SJIA.

Materials and methods

Patients and study subjects

Banked sera from 23 patients (14 boys, 9 girls) with SJIA according to the criteria
established by the International League of Association for Rheumatology [13] were obtained from the Department of Pediatrics, Yokohama City University School
of Medicine, Yokohama, Japan. All the patients were Asian and their mean age at the
start of the study was 7.25 ± 0.92 years. Eight of them had obtained a clinical response
to conventional therapy. Clinical response was defined as the absence of fever rash,
hepatosplenomegaly, and arthritis for at least 3 months, accompanied by normalization
of serum C-reactive protein. Briefly, conventional therapy consisted of three doses
of intravenous methylprednisolone (30 mg/kg per day) or oral prednisolone (1 to 2
mg/kg), followed by nonsteroidal anti-inflammatory drugs (NSAIDs) and a tapering dose
of oral prednisolone. In addition, methotrexate (2.5 to 5 mg/m2 per week orally) was used in three patients and cyclosporin (5 mg/kg per day orally)
in two. The mean period from acute status to complete clinical response was 27.7 ±
14.6 months. Fifteen patients who had inadequate response to the above therapy, as
well as to the addition of azathioprine (five patients), mizoribine (five patients),
sulfasalazine (two patients), or plasma exchange (three patients), had been administered
humanized anti-IL-6 receptor antibody (MRA; Chugai Pharmaceuticals, a subsidiary of
Roche Pharmaceuticals). All 15 patients had a clinical response to MRA. The mean period
from acute status to clinical response was 11.2 ± 5.1 months. Pretreatment sera were
collected before starting conventional treatment or giving the initial dose of MRA.
Post-treatment sera were collected 2 to 3 months after patients achieved a clinical
response. Ethical approval for this study was granted by Yokohama University. The
study was approved by both Chugai Pharmaceuticals and Roche Pharmaceuticals.

Statistical analysis

Peak clustering among sample groups was performed with the Biomarker Wizard (Ciphergen)
tool, with a peak detection threshold of 5 for signal-to-noise ratio, and mass tolerance
of 0.3%, for any peak appearing in at least 5% of experimental spectra being compared.
The Biomarker Wizard compares the mean intensity of peak clusters, by sample group,
using the nonparametric Mann–Whitney U test (two–way comparisons) and generates P values that reflect probabilities that mean peak intensities at a given m/z value differ by random chance. The intensity values of the automatically clustered
peaks (averaged between technical replicates of each sample) were used in classification
tree analysis (CART) using Ciphergen's Biomarker Patterns Software. This supervised
learning process uses cross-validation to optimize the minimization of classification
error.

Immunoprecipitation of SAA

A pooled sample from four sera taken before conventional treatment was incubated with
either Protein A–Sepharose beads alone or Protein A–Sepharose beads bound with 100
μg of anti-SAA antibody (Anogen, Yes Biotech Laboratories, Mississauga, ON, Canada).
After immunoprecipitation, the depleted serum was subjected to SELDI-TOF MS.

LC-ESI-MS/MS-TOF analysis

Protein identification by MS was carried out as previously described [14]. Briefly, serum samples were subjected to immunoprecipitation with anti- SAA or with
an IgG isotype control. Immunoprecipitates were washed extensively in phosphate-buffered
serum and centrifuged, and the pellet was sonicated for 10 min in 8 M urea/400 mM
NH4HCO3. The supernatant was diluted in water to a final concentration of 2 M urea/100 mM
NH4HCO3 and digested overnight at 37°C with 1 μg trypsin. The tryptic digest was subjected
to nano-LC-ESI-MS/MS analysis that was performed on a Q-TOF-2™ (Waters, Milford, MA,
USA), coupled on line to a CapLC system equipped with three separate syringe pump
modules, an auto injector, a 10-port valve and a 250-μm (inner diameter) × 1-mm pre-column.
Separations were performed on a 7-cm × 75-μm (inner diameter) capillary column. Both
columns were packed with Microsorb C18 (Varian, Mississauga, ON, Canada) reverse-phase
material. Peptides were eluted at a flow rate of 0.25 μl/min with the following linear
gradient of solvent B (80% aqueous acetonitrile with 10% isopropanol and 0.2% formic
acid) in solvent A: from 0 to 60% B in 40 min, to 90% B in 7 min, and to 10% B in
8 min. Spectra were acquired in auto MS/MS mode conducted using survey scans to choose
up to three precursor ions. Collision energies were selected automatically as a function
of m/z value and charge state. The Q-TOF mass spectrometer was calibrated by infusing a solution
of either NaI containing a small amount of cesium ion dissolved in 50% aqueous isopropanol
(0.2 μg/μl) or Glu-fibrinopeptide B (1 pmol/μl) dissolved in 30% aqueous acetonitrile
containing 0.2% formic acid. Protein identification was performed using the MASCOT
search program (Matrix Science Limited, http://www.matrixscience.comwebcite) and the NCBI (National Center for Biotechnology Information) (Bethesda, MD, USA)
protein database.

Results

To determine whether SELDI-TOF MS could be a valuable tool for analyzing serum protein
profiles in autoimmunity, we chose SJIA as a test model, since the disease has systemic
features, in addition to arthritis, likely to be reflected in the serum. Paired sera
were available from patients who had been followed up for a mean of 24.4 ± 6.9 months.
The availability of paired sera from each patient allowed for longitudinal comparison
and substantially reduced sample variability between the two groups. Sera from eight
patients with SJIA who responded to conventional therapy (therapy and definition of
clinical response are described in detail in the Materials and methods section) were
analyzed before and after therapy by SELDI-TOF MS using Ciphergen IMAC-3 (immobilized
metal affinity capture) copper chips (Fig. 1). All samples were run in duplicate. Table 1 shows the most significant differences between mass spectra of sera before and after
conventional therapy. Only variables with nonparametric P values of <0.001 are given. Of 282 spectral peaks identified, 23 had mean signal intensities
that were significantly different (P < 0.001) before and after response to treatment. These peaks represent potential biomarkers
of active disease. We next performed a similar analysis on paired sera from 15 patients
who had failed conventional therapy but responded to an experimental antibody to the
IL-6 receptor (MRA) [15]. These sera were obtained after failure of conventional therapy. Pre- and post-MRA
sera revealed similar profiles to those observed in the pre- and post-conventional
therapy group. Thus, substantial consistency was observed in protein profiles, regardless
of whether patients with active disease responded to conventional therapy or to MRA.
Eight of the differentially expressed peaks represent prominent, visually distinct
spectral features. These peaks are represented in Table 1 in bold, along with the number of paired patient sera in which each peak was differentially
expressed by visual inspection of the spectra. Representative examples of these peaks
are shown in Fig. 2.

Figure 1. SELDI-TOF MS technique. Serum samples are spotted onto IMAC-3 copper chips® (Ciphergen Biosystems). The chip is irradiated with a laser, resulting in ionization
of the adherent molecules. The ions travel through a vacuum tube and their mass-to-charge
ratios are calculated from their time of flight through the vacuum chamber. IMAC-3,
immobilized metal affinity capture; SELDI-TOF MS, surface-enhanced laser desorption/ionization
time-of-flight mass spectrometry.

Figure 2. SELDI-TOF MS profiles for patients with SJIA treated conventionally. Six serum protein
peaks can be clearly seen to have changed after conventional therapy. The profiles
of a representative patient are shown here. Visually distinct peaks that were clearly
different between pre- and post-treatment paired samples upon visual inspection of
the profiles (in bold type in Table 1) are outlined in grey. Pre- and post-treatment
spectra are shown on the same intensity scale in each frame. SELDI-TOF MS, surface-enhanced
laser desorption/ionization time-of-flight mass spectrometry SJIA, systemic juvenile
idiopathic arthritis.

To determine the usefulness of the profiles in classifying active versus controlled
SJIA, the data were subjected to CART (Biomarker Patterns Software, Ciphergen) analysis.
This sample classification method is designed, through multivariate analysis, to construct
classification trees recognizing a complex pattern of multiple peak intensities. The
method is ideally suited for sample sets large enough to permit cross-validation internal
to the 'training' data, but also the segregation of additional unused data as a validation
or 'testing' set. On these relatively small sample sets, CART was used in training
mode primarily as a data exploration tool. Whether using the training set as the MRA
group, or as the conventional treatment group, the CART analysis returned simple classification
trees consisting of one primary splitter, either 11.4 kDa or 11.6 kDa (m/z). The primary splitter at 11493 kDa correctly identified 13 of 14 pretreatment and
14 of 15 post-MRA treatment samples when conventional treatment was used as the training
set. When MRA treatment was used as the training set, all of the pre- and post-conventional
treatment samples were correctly identified as either pretreatment or post-treatment.
The distinction between these samples by CART registered at the most extreme level
of significance the program is capable of indicating. Even when forced to ignore the
mass spectrum peaks at 11493 or 11650 Da, the CART program was able to effectively
discriminate, using secondary peaks derived from them (at half these m/z values; attributed to doubly protonated species). This robust classification surpasses
the performance of any other sample set being profiled and analyzed by this and several
other statistical methods at this institution (data not shown).

Identification of serum amyloid A from SELDI-TOF MS mass spectra

A prominent group of peaks within the range 11.4 to 11.7 kDa m/z strongly distinguished the pre- and post-treatment samples (Fig. 3). The post-treatment groups showed an apparently single m/z peak at 11.75 kDa, which was also routinely observed in pooled reference sera from
healthy adults (data not shown). A previous study in nasopharyngeal cancer, using
the same SELDI-TOF technique and IMAC3 copper chip, identified two biomarkers, of
11.6 and 11.8 kDa, as serum amyloid A (SAA) [16]. Since SAA is a known biomarker of active SJIA [17], we compared the intensity of the 11.6-kDa peak with SAA levels in the sera, as determined
by latex agglutination. As shown in Fig. 4, a strong correlation was observed (R2 = 0.74), suggesting that the 11.6-kDa peak might represent SAA. To further investigate
the biochemical identity of this peak, serum containing high levels of the 11.6-kDa
peak was subjected to immunoprecipitation using anti-SSA antibody bound to Protein
A–Sepharose beads. As shown in Fig. 5, after immunoprecipitation and SELDI analysis, the 11.4- and 11.6-kDa peaks were
markedly diminished. To confirm the identity of the immunoprecipitated protein, anti-SAA
precipitates were digested with trypsin and subjected to η-scale liquid chromatography
electrospray ionization tandem mass spectrometry time-of-flight (LC-ESI-MC/MS-TOF)
analysis. Over 90 peptide ions were examined and only 2 proteins were identified,
including immunoglobulin and SAA. The SAA peptide ions represented approximately 51%
of the SAA sequence.

Figure 3. SELDI-TOF MS profiles for patients with SJIA before and after conventional or MRA
treatment. Sera taken before conventional and MRA treatment show similar patterns
that are distinct from the post-treatment profiles. Mean spectra of all patients are
shown in the 11- to 12-kDa m/z range. Means are compiled from 8 samples before and after conventional therapy and
15 samples before and after MRA therapy, each sample run in duplicate. Spectra were
preprocessed as described in the Materials and methods section. MRA, humanized anti-IL-6
receptor monoclonal antibody; SELDI-TOF MS, surface-enhanced laser desorption/ionization
time-of-flight mass spectrometry SJIA, systemic juvenile idiopathic arthritis.

Protein profiling by SELDI-TOF MS reveals patterns differentiating the responding
from the nonresponding SJIA group

The above data, using paired sera, demonstrate the ability of SELDI-TOF MS to identify
biomarkers of active disease, as exemplified by the identification of SAA. A long-term
goal is to predict clinical outcome, based on protein profiles present in the serum
early in the disease course. To begin to approach this challenge, we compared the
pretreatment serum profiles of the 8 patients who responded to conventional therapy
with those from the 15 patients who responded poorly to conventional therapy. Similar
to the preceding analysis, the latter samples were obtained after failure of conventional
therapy and before MRA treatment, when the patients still had active disease. In this
initial exploratory study, the number of available samples was too small for definitive
conclusions; however, several interesting trends were apparent. Several highly significant
differences were observed in the mass spectra of these sera, as shown in Table 2 and Fig. 6. These peaks may represent a profile predictive of response to conventional therapy.
Alternatively, they could represent the effects of conventional therapy or differences
between early versus long-standing disease. A number of peaks overlap with regions
observed in Table 1, including the region of SAA (11.6 kDa) as well as 4504 kDa and 28 kDa.

Figure 6. Differences between mass spectra of sera before and after MRA treatment for SJIA.
Sera were taken before and after treatment with MRA from patients for whom conventional
therapy had failed. The high significance of these differences suggests a profile
predictive of response to conventional therapy. The values represent mean intensities.
The P values of these univariate comparisons are given in Table 2. Error bars represent
standard deviations. MRA, humanized anti-IL-6 receptor monoclonal antibody; SJIA,
systemic-onset juvenile idiopathic arthritis.

We were fortunate to have pre-conventional treatment sera available from 3 of the
15 patients who responded poorly to conventional therapy and went on to receive MRA.
These three pretreatment sera were compared with the pretreatment sera of the eight
patients who responded to conventional therapy. Three peaks of interest were observed.
As shown in Fig. 7, all the nonresponders had lower values for the 4825-Da feature and higher values
for the 3276-Da and 3293-Da peaks than did the responders, with the exception of a
single outlier sample. However the sample size is small and this observation needs
further validation in a larger clinical cases series; this putative signature of nonresponse
may be susceptible to statistical overfitting, even at this level of analysis.

Figure 7. Serum proteins in SJIA patients according to whether they responded to conventional
therapy. Three most significant differences distinguishing between pretreatment samples
from conventional therapy responders (n = 8) and those from nonresponders (n = 3), suggesting a profile predictive of response to conventional therapy. The averaged
peak intensity is shown for the eight pretreatment 'responder' patient samples (left
panel) compared with the corresponding intensities of those same three peaks from
the three pretreatment 'nonresponder' patient samples (right panel). SJIA, systemic-onset
juvenile idiopathic arthritis.

Discussion

Current diagnostic techniques for rheumatic diseases are based on clinical presentation
and nonspecific serum markers. Because the phenotype of a rheumatic disease such as
SJIA is largely dependent on proteins, the present study was designed to determine
whether serum protein expression profiling with SELDI-TOF MS could be used to search
for new molecular diagnostic biomarkers and potential therapeutic targets. This approach
has theoretical advantages over other modalities used to identify differentially expressed
proteins. SELDI-TOF MS analysis is capable of detecting small amounts of protein,
hence the potential to detect proteins of relatively low abundance with affinity for
the ProteinChip surface. The technique is high throughput, allowing detection of hundreds
of species in a single sample, and is capable of analyzing large number of samples.
The data presented here show that it is possible to generate mass spectrometry protein
expression profiles from serum that can differentiate active versus controlled SJIA.

Some of the difficulties inherent in gene or protein expression profiling in human
disease include accounting for the genetic and environmental variability between patients
and the potential for detecting chance associations when measuring large numbers of
proteins or genes. The use of paired serum samples from individual patients in the
present study removes most of these variables and makes it likely that the changes
in protein profiles after successful treatment reflect the disease state rather than
confounding variables. We found a surprising degree of consistency in the relative
abundance of a number of serum proteins in ill versus well patients. The clear distinction
in the levels of these various ionic species between these sample groups permits a
robust classification based on simple thresholding on any one of a number of possible
variables.

One disadvantage of the SELDI-TOF MS technology is that protein sequences, and thus
specific identifications, are not obtained, requiring further biochemical/mass spectrometry
analysis to identify differentially-expressed proteins. A recent study using two-dimensional
gels and MALDI-TOF MS analysis of plasma and synovial fluids from patients with rheumatoid
arthritis or osteoarthritis also revealed the presence of SAA in samples from rheumatoid
arthritis but not osteoarthritis [18]. Although SELDI-TOF MS is not directly quantitative, it can detect changes in the
relative abundance of proteins in a manner that compares favorably to quantitative
methods such as latex agglutination or enzyme-linked immunosorbent assay. Identification
of SAA by SELDI-TOF MS helps validate our experimental approach, since SAA is a known
marker of active SJIA.

Although we were able to identify SAA by further analysis, there were many other peaks
observed in the serum profiles that have yet to be explored or identified. The 66.6-kDa
and 33.4-kDa peaks most likely represent serum albumin and its doubly protonated form,
as they are the correct mass and they increase after response to therapy, reflective
of the known rise in serum albumin levels in these patients (data not shown). Identification
of the other peaks is currently being investigated and may yield novel information
on the pathophysiology of SJIA. Furthermore, the proteins observed represent only
a fraction of those present in the serum. We observed only a subset of relatively
high-abundance proteins, limited by their concentration in the serum, their affinity
with the copper matrix of the IMAC ProteinChip, and the relative desorption/ionization
efficiencies of each protein. In addition, the fact that the mass spectra generated
in this study were from unfractionated sera is likely to obscure many protein species
that might otherwise be detectable in the absence of high-abundance serum proteins.
Refinement of the methodologies for processing serum samples, including initial depletion
of high-abundance proteins, is likely to substantially increase the information that
can be derived from the resulting profiles.

There are likely many more subtypes of the group of diseases known collectively as
idiopathic arthritis than have as yet been defined by clinical criteria. The ability
to differentiate uncontrolled from controlled SJIA by serum protein profiling raises
the possibility of more specific diagnostic and prognostic criteria for evaluating
such patients. Furthermore, the dramatic mass spectral differences observed between
the sample groups led us to compare sera obtained before any treatment, from patients
who ultimately differed in their response to conventional therapy. While the current
work can only comment on an anecdotal basis from a limited number of these samples,
some early differences were observed that suggest that a prognostic profile might
exist. Beyond the obvious clinical usefulness of such a profile, it also could provide
a discovery tool for further characterization of the pathophysiology of SJIA.

Although SELDI-TOF MS was recently used to compare synovial fluids from patients with
rheumatoid arthritis and osteoarthritis [19], the present study is, to our knowledge, the first to define a serum proteomic profile
of a rheumatic disease using SELDI-TOF MS. The SELDI-TOF MS technique described here
provides a rapid, high throughput, and mass accurate method for detecting relative
quantities of multiple disease-related proteins simultaneously. Using this platform,
we identified a protein (SAA) known to be elevated in active SJIA. This proteomic
profiling approach has the potential to expand the current repertoire of molecular
targets and to provide diagnostic and prognostic information useful for improving
the care of and ultimate outcome for SJIA patients.

Conclusion

This study demonstrates the presence of serum proteomic profiles in SJIA that are
reflective of active disease and suggests the feasibility of using the SELDI-TOF MS
platform used as a tool for proteomic profiling in autoimmune diseases. Furthermore,
the study validates the ability of the SELDI-TOF MS platform to identify a known biomarker
of SJIA (SAA), suggesting that it may also be useful as a screening approach towards
the discovery of novel biomarkers. To that end, identifying the 22 unknown m/z protein species in the serum profiles of our patients is now the focus of further
investigation.

Competing interests

The author(s) declare that they have no competing interests.

Authors' contributions

TM, DM, BL, and RH participated in all experimental design, data collection, and analysis
and helped draft the manuscript. MM, TI, SY, and NN provided patient sera and clinical
data. MW carried out the sample preparation. KK and AV carried out the LC ESI-MS/MS-TOF
analysis and helped draft the manuscript. All authors read and approved the final
manuscript.